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Technology Transfer and Early Industrial Development: Evidence from the Sino-Soviet Alliance

Mon, 01 Jan 0001 00:00:00 +0000

This paper estimates the causal effect of technology and knowledge transfers on early industrial development using the Sino-Soviet Alliance of the 1950s as a natural experiment. Between 1950 and 1957, the Soviet Union supported the “156 Projects” — 139 approved civil projects for constructing technologically advanced, large-scale, capital-intensive industrial facilities in China. The intended program comprised two components: a “basic” transfer of Soviet state-of-the-art machinery and equipment (including blueprints, site surveys, and plant construction assistance), and an “advanced” know-how transfer involving Soviet experts residing in Chinese plants for roughly three years to train engineers and production supervisors in organizational, technological, and planning methods. Total investment amounted to approximately $80 billion in 2020 figures (45.7% of Chinese GDP in 1949).

Identification exploits idiosyncratic delays in project completion caused by Soviet production capacity constraints, insufficient experts, translator shortages, and miscommunication — factors documented in historical records as unrelated to project-specific characteristics. When the Sino-Soviet Split in 1960 abruptly ended the program, all 139 plants had been built but differed in what transfers they had received: 46 received both machinery and know-how (advanced), 46 received only machinery (basic), and 47 received neither (comparison). The paper verifies, via ANOVA tests, multinomial logit models, balancing regressions on 26 plant characteristics, pre-trend tests, and Oster (2019) selection-on-unobservables bounds, that the three groups were statistically equivalent prior to receiving the Soviet transfers.

The primary data source is plant-level annual reports from the Steel Association covering 94 steel firms (1,410 plants) from 1949 to 2000, matched to 304 steel plants across the 156 Projects. Supplementary sources include the declassified 1985 Second Industrial Survey (7,592 largest Chinese firms) and the China Industrial Enterprises database (1998–2013, over 1 million firms).

Three main results emerge. First, receiving only the basic (machinery) transfer had positive but short-lived effects: output of basic plants peaked at 14.7 percent above comparison plants six years after receiving Soviet machinery, then declined monotonically and became statistically insignificant after 20 years — consistent with the estimated 15–20 year life cycle of Soviet capital. Second, the advanced transfer had large and persistent effects: advanced plants’ output rose 8.4 percent relative to basic plants within two years, 19.7 percent within 20 years, and 49.5 percent cumulatively after 40 years. TFPQ of advanced plants reached 47.9 percent above basic plants after 40 years. These magnitudes held across industries in 1985 and 1998–2013 data, where value added of advanced firms was 41.4–52.0 percent higher and TFPR 39.5–49.3 percent higher than basic firms. Third, the program generated horizontal spillovers (12.9 percent higher output, 12.4 percent higher productivity for steel plants in counties hosting advanced plants) and vertical spillovers (16.4 percent productivity gain for supply-chain firms in counties of advanced nonsteel plants), with spillover effects conditional on post-1990s market liberalization to materialize in private firms.

The mechanism driving persistence is the accumulation of organizational and human capital during the advanced transfer, which enabled advanced plants — uniquely — to develop new production processes endogenously, home-fabricate continuous casting furnaces to replace obsolete Soviet open-hearth equipment, and produce export-quality steel. Advanced plants employed more engineers and high-skilled technicians, established professional schools, and their counties had 10.4 percent higher STEM university degree rates and 16.8 percent more technical schools.

Scope conditions: results apply to large-scale, capital-intensive state-planned industrial facilities in a country at an early stage of industrialization, under conditions of near-complete trade isolation (1960–1978) that prevented basic plants from compensating via imported foreign capital. The estimated aggregate contribution of the program is that, without both transfer types, Chinese real GDP per capita growth between 1953 and 1978 would have been 7 to 19 percent lower.

Q: What distinguishes the “basic” from the “advanced” Soviet transfer? A: The basic transfer involved duplication of whole Soviet plants through provision of state-of-the-art Soviet machinery, equipment, blueprints, geological surveys, and construction assistance. The advanced transfer added visits of Soviet experts — expected to stay approximately three years — to teach Chinese technicians how to operate the machinery and to provide within-firm training in engineering (math, physics, chemistry, organizational and planning methods) and supervisory management based on “scientific management” principles including quality-control methods.

Q: What caused plants to receive different levels of transfer, and why is this variation credible for identification? A: Delays arose from Soviet production capacity constraints (by 1955, one-third of annual Soviet steel-rolling output was destined for China), insufficient experts, translator shortages, and bilateral miscommunication — all documented in historical records as unrelated to project characteristics. When the 1960 Split ended the program, plants’ treatment status was determined by where they happened to be in the delivery queue. ANOVA tests find no significant differences in approval year, investment, workforce, equipment value, project length, or capacity across the three groups, and a multinomial logit on province and industry fixed effects shows no group had higher ex-ante probability of receiving either transfer type.

Q: What were the output effects of the basic transfer, and why did they fade? A: Output of basic plants was not significantly above comparison plants for the first two years, peaked at 14.7 percent higher six years after receiving Soviet machinery, then declined monotonically and became statistically insignificant after 20 years. This timing corresponds to the estimated 15-year life cycle of Soviet capital goods. TFPQ of basic plants followed the same pattern, peaking at 14.5 percent above comparison plants. Without the know-how component, basic plants could not develop new processes or home-fabricate replacement capital, so productivity advantages disappeared as Soviet equipment became obsolete.

Q: What were the output and productivity effects of the advanced transfer? A: Advanced plants’ output rose 8.4 percent relative to basic plants within two years of the Soviet transfer and 19.7 percent within 20 years, reaching a cumulative effect of 49.5 percent after 40 years. TFPQ of advanced plants increased from 8.3 percent above basic plants two years after the transfer to 47.9 percent after 40 years. These effects were driven by output growth rather than differential input use — the number of workers, coke, and iron were statistically indistinguishable across the three plant types — ruling out government input reallocation as an explanation.

Q: Did the advanced transfer affect steel quality? A: Advanced plants produced substantially more crude steel (higher quality, lower carbon content) and less pig iron than basic and comparison plants, and this quality advantage persisted well beyond the 20-year life cycle of Soviet capital. Basic plants also shifted toward crude steel initially but the quality advantage dissipated once Soviet machinery became obsolete, whereas advanced plants maintained the shift through adoption of the basic oxygen process and later continuous casting furnaces.

Q: What is the main mechanism through which the advanced transfer generated persistent effects? A: The advanced transfer equipped engineers and supervisors with organizational, technological, and planning knowledge, enabling advanced plants to develop and adopt the basic oxygen steelmaking process independently during China’s 1960–1978 period of trade isolation. Advanced plants had a 15.2 percent higher probability of using the basic oxygen process five years after the transfer and a 65.1 percent higher probability twenty years after, relative to basic plants. They also home-fabricated continuous casting furnaces, making them 26.7 to 78.4 percent more likely to use such furnaces 10 to 20 years after the transfer; basic plants showed no differential advantage over comparison plants on this measure.

Q: What role did trade openness play in the divergence between basic and advanced plants? A: Once China opened to international trade from 1978, advanced plants relied dramatically less on imported foreign capital than basic plants — likely because they had developed domestic production capabilities. At the same time, advanced plants exported 45.5 percent more steel and produced 51.1 percent more steel above international quality standards than basic plants. Basic plants showed no differential imports of foreign capital or differential exports relative to comparison plants, suggesting that once both types could access foreign machinery, basic plants lost any remaining productivity edge.

Q: What were the human capital effects of the advanced transfer? A: Over time, advanced plants opened training schools for high-skilled technicians and offered within-firm training programs for engineers. As a result, advanced plants employed more engineers and high-skilled technicians and fewer low-skilled workers than basic plants, while the human capital composition did not differentially change between basic and comparison plants. At the county level, universities hosting advanced plants were 10.4 percent more likely to offer STEM degrees, had 16.8 percent more technical schools, 14.3 percent more STEM college graduates, and 17.6 percent more high-skilled workers than counties hosting basic plants.

Q: Did the government differentially favor basic or advanced plants after the Split? A: The paper finds no evidence of special government favor. Government transfers and loans were not differentially allocated to basic or advanced plants in either the short or long run. Distance from railroads and roads did not change differentially across plant types. Measures of political connection and politician quality at the prefecture level showed no significant differences across the three groups in the 40 years after the Soviet transfer. County-level total investment and investments in related and unrelated industries were also statistically indistinguishable.

Q: What were the intra-firm spillover effects? A: Steel plants in the same firm as advanced plants increased their steel production by 24.9 percent and were 22.1 percent more productive relative to plants in the same firm as basic plants, after the Soviet transfer. Plants in the same firm as basic plants showed no differential performance relative to plants in the same firm as comparison plants. The within-firm spillovers appear driven by the transmission of new technologies and production methods through formal within-firm training programs, as supported by historical records.

Q: What were the horizontal spillover effects across firms? A: Steel plants in the same counties as advanced plants produced 12.9 percent higher output and were 12.4 percent more productive than those in counties hosting basic plants, after the transfer. They were more likely to adopt basic oxygen converters and continuous casting furnaces, and from 1978 they exported significantly more and produced more steel above international quality standards, mirroring the patterns of the advanced plants themselves.

Q: What were the vertical spillover effects? A: Steel plants in counties hosting nonsteel basic plants produced 14.2 percent more steel than those in counties hosting nonsteel comparison plants, suggesting some output spillover from basic machinery. However, only plants in counties of advanced nonsteel plants experienced a productivity increase — estimated at 16.4 percent — relative to plants in counties of basic nonsteel plants. These supply-chain firms were also the only ones to show increased adoption of basic oxygen and continuous casting furnace technology and differential engagement in trade.

Q: How did market liberalization reforms interact with the spillover effects? A: Starting in the late 1990s, privatized firms economically related to advanced plants outperformed their counterparts in terms of value added, TFPR, and exports, while state-owned firms in the same counties no longer showed a competitive advantage. New private firms locating in counties that had hosted advanced plants received an additional performance gain. At the county level, counties hosting advanced plants had on average 16.6 percent more private firms and 25.2 percent more privately-produced industrial output than counties hosting basic plants. The mechanism appears to be the stock of industry-specific human capital concentrated in those counties, which private firms could draw on once allowed to compete for workers.

Q: What is the estimated aggregate contribution of the Soviet transfer to Chinese growth? A: Province-level regressions show that each additional basic project increased province-level output by 1.1 percent per year on average, and each additional advanced project by 6.2 percent per year. A back-of-the-envelope calculation implies that without both transfer types, Chinese real GDP per capita growth between 1953 and 1978 would have been 7 to 19 percent lower.

Q: How does the paper rule out selection on unobservable characteristics? A: Using the Oster (2019) methodology, the paper finds that for the treatment effects to become statistically insignificant, selection on unobserved variables would need to be 8 to 19 times larger than selection on observed variables — a range the authors characterize as implausible given the strong balancing on observables and the historical documentation of delay causes.

Q: How does this paper differ from Heblich et al. (2020), which also studies Sino-Soviet technology transfer? A: Heblich et al. (2020) study long-run negative spillovers of the 156 Projects on counties that hosted them relative to counties that were geographically suitable but ultimately not selected, focusing on an outside-the-program comparison. This paper instead exploits within-program variation — differences across the three plant types — using plant-level data to assess short-, medium-, and long-run direct effects and spillover effects of different transfer intensities.

Basic Transfer: The provision of Soviet state-of-the-art machinery, equipment, blueprints, geological surveys, and plant construction assistance — duplicating a whole Soviet plant — without accompanying human capital or organizational training.

Advanced Transfer: The full Soviet technology and know-how package: basic machinery provision plus multi-year visits of Soviet experts who taught Chinese engineers and production supervisors organizational, technological, and planning methods based on “scientific management” principles.

Comparison Plants: Plants approved under the 156 Projects that received neither Soviet machinery nor technical assistance due to delays compounded by the Split, and which continued operating with traditional domestic technology.

156 Projects: An array of 139 approved, technologically advanced, large-scale, capital-intensive industrial facilities whose construction the Soviet Union agreed to support between 1950 and 1957 as part of the Sino-Soviet Alliance, representing 45.7% of Chinese GDP in 1949.

Tacit Knowledge: Industry- and firm-specific knowledge embodied in workers and organizations — including operational methods, quality-control procedures, and process innovation capabilities — that cannot be transferred through capital goods alone and requires extensive on-the-job training from foreign experts.

Basic Oxygen Process: A steelmaking process innovation that became predominant in the 1960s by blowing oxygen through molten pig iron to reduce carbon content; adopted by advanced plants through endogenous process development, while basic plants showed no differential adoption relative to comparison plants.

Source Text Origin: The paper’s classification scheme for the grounding of evidence — in this case, full working paper text obtained from NBER WP 29455, enabling comprehensive summary of quantitative results, mechanisms, and robustness tests.

Trust and Innovation Within the Firm

Mon, 01 Jan 0001 00:00:00 +0000

This paper investigates whether and how a CEO’s inherited generalized trust enhances innovation within firms, offering a micro-foundation for the well-documented macro-level relationship between societal trust and economic growth. The author argues that trust — by inducing tolerance of failure — encourages researchers to undertake high-risk, explorative R&D rather than safe exploitation of known approaches.

The empirical foundation is a matched CEO-firm-patent dataset covering 5,753 CEOs at 3,598 US public firms during 2000–2011, encompassing 700,000 patents and over one million inventors. CEO trust is measured as an inherited trait: each CEO’s ethnic origin is inferred probabilistically from their last name using de-anonymized US censuses from 1910–1940, and ethnic-origin-specific trust levels are drawn from the US General Social Survey (GSS), restricted to respondents in highly prestigious occupations. The resulting trust measure is the weighted average of ethnic-specific trust scores across a CEO’s likely ethnic composition.

The main empirical strategy exploits within-firm variation across CEO transitions, using firm and year fixed effects to compare patenting before and after a CEO change. The identifying assumption — that the timing of CEO transitions and the new CEO’s trust level are not predicted by prior firm patenting trends — is supported by event-study tests showing flat pre-trends. A one-standard-deviation increase in CEO inherited generalized trust (equivalent to the difference between Greek and English averages) is associated with a 6.2–6.3% increase in patent filings, statistically significant at the 1% level. For the average firm, this equals approximately 1.1 additional patents annually, worth roughly $6.8 million. The effect is larger among exogenous transitions (CEO retirement or death): 8.5% in the restricted sample, and an IV estimate of 8.2%. The back-of-envelope calculation suggests this trust-innovation channel could account for approximately 37% (range: 16–58%) of the effect of trust on GDP per capita growth.

The paper’s central mechanism — risk taking — is tested by examining the distribution of patent quality rather than the mean. Under the risk-taking mechanism, trust should increase the variance of R&D project quality, raising high-quality patents without necessarily increasing low-quality ones. Consistent with this, CEO trust raises only above-median quality patents (measured by forward citation decile), with effects increasing monotonically toward the top decile and no statistically significant effect on below-median patents. Average patent quality as measured by citation-weighted counts or patent value rises by 4–6%. Trust also disproportionately raises the share of explorative patents (those with at least 90% of backward citations outside the firm’s existing knowledge stock) by 1 percentage point over a base of 17%.

The transmission channel is examined using BERT-based classification of nearly one million Glassdoor employee reviews. Under more trusting CEOs, firms exhibit stronger top-down trust sentiment (managers trusting workers), particularly among R&D workers and scientists. The effect materializes within the first two years of a CEO term. Director selection provides an additional transmission mechanism: under more trusting CEOs, newly appointed directors are more trusting and departing directors are less trusting.

A within-CEO design using bilateral trust (toward researchers in specific countries) with CEO fixed effects addresses omitted CEO characteristics. A one-standard-deviation increase in CEO bilateral trust toward a country is associated with a 5% increase in patents by inventors in that country’s R&D lab, controlling for firm-by-year, CEO, and inventor-country fixed effects.

The effect is strongest when CEO trust is matched to a high-quality researcher pool; in firms with mostly low-quality researchers, high trust may be counterproductive. Trust is also a substitute for R&D knowledge: the effect disappears when the CEO holds a non-MBA graduate degree or has prior R&D experience.

Q: What is the main research question? A: The paper asks whether a CEO’s generalized trust causes more and higher-quality innovation within the firm, and through what mechanism. It also asks how trust transmits from the CEO to researchers who rarely interact with the CEO directly.

Q: How is CEO trust measured? A: CEO trust is measured as an inherited trait using a two-step procedure. First, each CEO’s last name is probabilistically mapped to one or more ethnic origins using four de-anonymized US censuses (1910–1940). Second, ethnic-origin-specific trust is computed from GSS respondents in highly prestigious occupations. The CEO’s trust measure is the weighted average across ethnic compositions. This measure is shown to be more precise than an individual-level survey measure and approximately 80% as precise as a game-based measure, without introducing attenuation bias.

Q: What is the baseline patent effect and how large is it economically? A: A one-standard-deviation increase in CEO inherited trust is associated with a 6.2–6.3% increase in patent filings (statistically significant at 1%). For the average baseline firm, this is approximately 1.1 additional patents per year, valued at roughly $6.8 million. When patent quality is accounted for, the effect rises to 9.9% using citation-weighted patent count and 11.5% using patent value based on excess stock returns on grant dates.

Q: Is the effect causal? What identification strategy is used? A: The main strategy uses firm and year fixed effects, identifying the effect from within-firm variation around CEO transitions. Pre-trend tests confirm that neither the timing of CEO changes nor the new CEO’s trust level predicts prior firm patenting. Among exogenous transitions (CEO retirements and deaths), the effect is 8.5%, and an IV estimate using the predecessor’s trust as instrument yields 8.2% (significant at 10%), both comparable to the baseline.

Q: What is the macroeconomic significance of the trust-innovation channel? A: Combining the paper’s trust-to-patents estimate (0.042–0.062) with Akcigit et al.’s (2017) patents-to-GDP-growth estimate (0.026–0.066) and the cross-country trust-to-growth coefficient (0.007), the trust-innovation channel could explain approximately 37% of the effect of trust on growth, with a plausible range of 16–58%.

Q: What is the mechanism linking CEO trust to innovation? A: The conceptual mechanism is that a more trusting manager interprets researcher failure as bad luck rather than bad type, making her more likely to tolerate failure and continue employing the researcher. This increases the researcher’s incentive to pursue explorative, high-risk R&D over safe exploitation of known approaches. The mechanism implies a variance-increasing effect on the R&D quality distribution, rather than a mean shift.

Q: How is the risk-taking mechanism tested against alternative mechanisms? A: The paper examines the distribution of patent quality by citation decile. Under mean-shifting alternatives (delegation, cooperation, relational contracting), trust should raise all quality brackets. Under risk-taking, trust raises only high-quality patents. The results show CEO trust has monotonically increasing effects from low to high quality deciles, with no statistically significant effect on below-median patents, consistent only with the variance-increasing (risk-taking) mechanism.

Q: What patent quality measures are used and what do they show? A: Beyond forward citation deciles, the paper uses explorativeness (patents with at least 90% of backward citations outside the firm’s existing knowledge stock), disruptiveness (Funk and Owen-Smith, 2017), patent importance (Kelly et al., 2021), backward citations to scientific literature, and patent scope. Trust increases all these measures with statistically significant positive coefficients. The share of explorative patents rises by 1 percentage point over a base of 17%. Average citation count and patent value increase by 4–6%.

Q: Does CEO trust raise R&D expenditure? A: No. The coefficients from regressing R&D expenditure on CEO trust are neither statistically significant nor large enough to explain the innovation effect. The patent effect is also robust to controlling for R&D inputs, suggesting that trust affects the type of projects chosen (consistent with risk-taking) or their realized outcomes, rather than the scale of R&D.

Q: How does CEO trust transmit to corporate culture? A: Using BERT-based classification of nearly one million Glassdoor reviews covering 266 firms and 397 CEO terms between 2008 and 2017, the paper finds that CEO trust is associated with stronger top-down trust sentiment (managers trusting workers). The normalized effect of a one-standard-deviation increase in CEO trust on overall trust sentiment is 0.257, on top-down trust 0.531, and on bottom-up trust only 0.141 (statistically insignificant). The effect is strongest among reviewers who identify as scientists, researchers, or engineers, and materializes within the first two years of the CEO term.

Q: What evidence exists for transmission via director selection? A: Under more trusting CEOs, newly appointed directors — especially those who remain until the end of the CEO term — are more trusting, and departing directors are less trusting. The average director trust improves during the CEO’s term. Because 54% of director hirings and 46% of turnovers occur within the first two years, this change also materializes quickly, consistent with the dynamic pattern of trust culture change.

Q: What is the within-CEO bilateral trust result and what does it add? A: Using within-CEO variation in bilateral trust toward researchers from different countries (from Eurobarometer surveys), and controlling for CEO, inventor-country, and firm-by-year fixed effects, a one-standard-deviation increase in CEO bilateral trust toward a country is associated with a 5% increase in patents by inventors in that country’s R&D lab. This design allows CEO fixed effects, ruling out unobserved CEO-level confounders such as management style or R&D ability.

Q: When is CEO trust counterproductive? A: CEO trust is beneficial only when matched to a high-quality researcher environment. Using residual patent output (controlling for observable firm and CEO characteristics) as a proxy for researcher quality, the effect of CEO trust on patents, patent output per R&D dollar, and future sales/employment/TFP is significant only among firms in the top two quintiles of researcher quality. In firms with mostly low-quality researchers, high CEO trust may be counterproductive by failing to screen out bad researchers.

Q: How does the trust effect vary by industry and CEO background? A: The effect is ubiquitous across industries but especially pronounced in pharmaceutical and ICT firms. The timing varies: it manifests quickly in ICT (short R&D lag) and more slowly in pharma (long R&D horizon). The effect vanishes when the CEO holds a non-MBA graduate degree or has prior R&D experience, suggesting trust is a substitute for direct knowledge of R&D processes.

Q: Are the results robust? A: Yes. The paper reports 14 categories of robustness checks including alternative patent transformations, alternative trust measures (LASSO, World Value Survey, Global Preference Survey, alternative GSS questions), alternative standard error clustering, Poisson count models, restriction to granted patents, exogenous transition subsamples, modern difference-in-differences estimators (de Chaisemartin et al., 2024; Sun and Abraham, 2021; Callaway and Sant’Anna, 2021; Borusyak et al., 2024), and leave-one-ethnicity-out. The baseline result is stable across all these checks.

Inherited generalized trust: The paper’s measure of a CEO’s trust disposition, defined as the probability-weighted average of ethnic-origin-specific trust levels (from the GSS) based on the CEO’s likely ethnic composition inferred from their last name and historical census records. It captures the culturally transmitted component of trust, distinct from individual-level noise.

Explorative R&D: In the paper’s framework (building on March, 1991), research activities that involve testing untested paths, carrying high risk of failure but high potential for innovation, as opposed to exploitation of well-known approaches with low failure risk. The paper argues CEO trust encourages researchers to shift toward exploration.

Tolerance of failure: A manager’s propensity to attribute a researcher’s failure to bad luck rather than bad type. Under the paper’s mechanism, a more trusting manager gives greater weight to bad luck, making her more likely to retain the researcher after failure, thereby incentivizing risk taking.

Top-down trust: In the paper’s BERT-based classification of Glassdoor reviews, the direction of trust from managers toward workers (as opposed to bottom-up trust from workers toward managers). The paper finds CEO trust primarily raises top-down trust sentiment, especially among R&D workers.

Patent explorativeness: A patent quality measure defined as the share of its backward citations that fall outside the firm’s existing knowledge stock; patents are classified as explorative if at least 90% of backward citations are outside that stock. The paper uses this as a direct measure of explorative R&D output.

Bilateral trust: CEO d’s directed trust toward individuals from country c, computed analogously to inherited generalized trust but using Eurobarometer survey data on country-pair trust attitudes among European-origin populations. Used in the within-CEO design to control for CEO fixed effects.

Variance-increasing mechanism: The paper’s characterization of the risk-taking channel, in which CEO trust raises the variance (not the mean) of the R&D project quality distribution by encouraging researchers to pursue high-risk, high-reward exploration. Empirically identified by the pattern that trust raises only above-median quality patents with monotonically increasing effects toward the top decile.